{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RNN 用于时间序列的分析\n",
    "前面我们讲到使用 RNN 做简单的图像分类的问题，但是 RNN 并不擅长此类问题，下面我们讲一讲如何将 RNN 用到时间序列的问题上，因为对于时序数据，后面的数据会用到前面的数据，LSTM 的记忆特性非常适合这种场景。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先我们可以读入数据，这个数据是 10 年飞机月流量，可视化得到下面的效果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_csv = pd.read_csv('./data.csv', usecols=[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1168e7588>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pKrEZfefg6JQXdhNRXWCUkFoXZP/+8QP0eQN87fo1gLECtrnPS7HbldTOT+W5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\nLHES6IUQYomTQC+EEEvc/wMu3j163KHs+AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11571ff98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data_csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先我们进行预处理，将数据中 `na` 的数据去掉，然后将数据标准化到 0 ~ 1 之间。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 数据预处理\n",
    "data_csv = data_csv.dropna()\n",
    "dataset = data_csv.values\n",
    "dataset = dataset.astype('float32')\n",
    "max_value = np.max(dataset)\n",
    "min_value = np.min(dataset)\n",
    "scalar = max_value - min_value\n",
    "dataset = list(map(lambda x: x / scalar, dataset))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接着我们进行数据集的创建，我们想通过前面几个月的流量来预测当月的流量，比如我们希望通过前两个月的流量来预测当月的流量，我们可以将前两个月的流量当做输入，当月的流量当做输出。同时我们需要将我们的数据集分为训练集和测试集，通过测试集的效果来测试模型的性能，这里我们简单的将前面几年的数据作为训练集，后面两年的数据作为测试集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def create_dataset(dataset, look_back=2):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset) - look_back):\n",
    "        a = dataset[i:(i + look_back)]\n",
    "        dataX.append(a)\n",
    "        dataY.append(dataset[i + look_back])\n",
    "    return np.array(dataX), np.array(dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建好输入输出\n",
    "data_X, data_Y = create_dataset(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 划分训练集和测试集，70% 作为训练集\n",
    "train_size = int(len(data_X) * 0.7)\n",
    "test_size = len(data_X) - train_size\n",
    "train_X = data_X[:train_size]\n",
    "train_Y = data_Y[:train_size]\n",
    "test_X = data_X[train_size:]\n",
    "test_Y = data_Y[train_size:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最后，我们需要将数据改变一下形状，因为 RNN 读入的数据维度是 (seq, batch, feature)，所以要重新改变一下数据的维度，这里只有一个序列，所以 batch 是 1，而输入的 feature 就是我们希望依据的几个月份，这里我们定的是两个月份，所以 feature 就是 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "train_X = train_X.reshape(-1, 1, 2)\n",
    "train_Y = train_Y.reshape(-1, 1, 1)\n",
    "test_X = test_X.reshape(-1, 1, 2)\n",
    "\n",
    "train_x = torch.from_numpy(train_X)\n",
    "train_y = torch.from_numpy(train_Y)\n",
    "test_x = torch.from_numpy(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from torch import nn\n",
    "from torch.autograd import Variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里定义好模型，模型的第一部分是一个两层的 RNN，每一步模型接受两个月的输入作为特征，得到一个输出特征。接着通过一个线性层将 RNN 的输出回归到流量的具体数值，这里我们需要用 `view` 来重新排列，因为 `nn.Linear` 不接受三维的输入，所以我们先将前两维合并在一起，然后经过线性层之后再将其分开，最后输出结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 定义模型\n",
    "class lstm_reg(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, output_size=1, num_layers=2):\n",
    "        super(lstm_reg, self).__init__()\n",
    "        \n",
    "        self.rnn = nn.LSTM(input_size, hidden_size, num_layers) # rnn\n",
    "        self.reg = nn.Linear(hidden_size, output_size) # 回归\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x, _ = self.rnn(x) # (seq, batch, hidden)\n",
    "        s, b, h = x.shape\n",
    "        x = x.view(s*b, h) # 转换成线性层的输入格式\n",
    "        x = self.reg(x)\n",
    "        x = x.view(s, b, -1)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = lstm_reg(2, 4)\n",
    "\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=1e-2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "定义好网络结构，输入的维度是 2，因为我们使用两个月的流量作为输入，隐藏层的维度可以任意指定，这里我们选的 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 100, Loss: 0.00395\n",
      "Epoch: 200, Loss: 0.00337\n",
      "Epoch: 300, Loss: 0.00259\n",
      "Epoch: 400, Loss: 0.00149\n",
      "Epoch: 500, Loss: 0.00109\n",
      "Epoch: 600, Loss: 0.00106\n",
      "Epoch: 700, Loss: 0.00097\n",
      "Epoch: 800, Loss: 0.00092\n",
      "Epoch: 900, Loss: 0.00087\n",
      "Epoch: 1000, Loss: 0.00105\n"
     ]
    }
   ],
   "source": [
    "# 开始训练\n",
    "for e in range(1000):\n",
    "    var_x = Variable(train_x)\n",
    "    var_y = Variable(train_y)\n",
    "    # 前向传播\n",
    "    out = net(var_x)\n",
    "    loss = criterion(out, var_y)\n",
    "    # 反向传播\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    if (e + 1) % 100 == 0: # 每 100 次输出结果\n",
    "        print('Epoch: {}, Loss: {:.5f}'.format(e + 1, loss.data[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练完成之后，我们可以用训练好的模型去预测后面的结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net = net.eval() # 转换成测试模式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_X = data_X.reshape(-1, 1, 2)\n",
    "data_X = torch.from_numpy(data_X)\n",
    "var_data = Variable(data_X)\n",
    "pred_test = net(var_data) # 测试集的预测结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 改变输出的格式\n",
    "pred_test = pred_test.view(-1).data.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x120f7f358>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qRMMBalX54l07YP58Kenbvr17rb57dapDx44i7K5iL507i7MRlLhHRIg3PmeO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120f56d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出实际结果和预测的结果\n",
    "plt.plot(pred_test, 'r', label='prediction')\n",
    "plt.plot(dataset, 'b', label='real')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里蓝色的是真实的数据集，红色的是预测的结果，我们能够看到，使用 lstm 能够得到比较相近的结果，预测的趋势也与真实的数据集是相同的，因为其能够记忆之前的信息，而单纯的使用线性回归并不能得到较好的结果，从这个例子也说明了 RNN 对于序列有着非常好的性能。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**小练习：试试改变隐藏状态输出的特征数，看看有没有什么改变，同时试试使用简单的线性回归模型，看看会得到什么样的结果**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
